ABSTRACT
The scenario of online learning is a very urgent need in the world of future knowledge. Since the Corona Virus Disease-19 pandemic, the world economy has started to plummet and caused many adults to lose their jobs. The advantage is the flexibility and rapid development of the internet. In 2020, the number of unemployed increased significantly. This reason makes people strive to improve their ability to meet job requirements by taking online courses. Online courses are a way that people can choose to improve their skills anywhere and anytime. The sustainability of online course material that is offered to the course user and issued by the company will be discussed in this study. The novelty of this research is to obtain a decision support model based on fuzzy logic for determining online courses. The method used is decision-making based on UML and fuzzy logic for the final decision. The fuzzy inference model process begins by determining the decision parameters then using fuzzification with absolute input then refracted with fuzzy criteria, and ends with defuzzification with absolute output. There are two groups of parameters in this study, company profits which consist of 5 parameters and user benefits, which consist of 9 parameters. Once the model is verified and valid, the final decision is useful for users looking for online course and also useful for the decision unit of online course companies in determining the sustainability of online course materials. © 2022 IEEE.
ABSTRACT
Because India's economy has shrunk to a low level during COVID-19, building an emergency decision support model (EDSM) for economic growth factors is the main objective of this study. We develop the TODIM-VIKOR method under Pythagorean fuzzy information. For dealing with comparison problems, the Pythagorean fuzzy scoring function is presented. We also include a new entropy metric for assessing the degree of fuzziness in PyFS. We also present a new Jensen Shannon divergence metric for PyFS that can be used to compare the discrimination information of two PyFSs. In this article, we introduced entropy and divergence measures to derive objective weight in the TODIM-VIKOR approach. Establishes a novel emergency decision making (EDM) strategy under the Pythagorean fuzzy atmosphere, using economic growth considerations. We used TODIM to determine the overall dominance degree, which takes into account the bounded rationality of decision makers, and VIKOR to calculate the compromise ranking of alternatives. © 2023 IEEE.
ABSTRACT
Reliable prediction of natural gas consumption helps make the right decisions ensuring sustainable economic growth. This problem is addressed here by introducing a hybrid mathematical model defined as the Choquet integral-based model. Model selection is based on decision support model to consider the model performance more comprehensively. Different from the previous literature, we focus on the interaction between models when combine models. This paper adds grey accumulation generating operator to Holt-Winters model to capture more information in time series, and the grey wolf optimizer obtains the associated parameters. The proposed model can deal with seasonal (short-term) variability using season auto-regression moving average computation. Besides, it uses the long short term memory neural network to deal with long-term variability. The effectiveness of the developed model is validated on natural gas consumption due to the COVID-19 pandemic in the USA. For this, the model is customized using the publicly available datasets relevant to the USA energy sector. The model shows better robustness and outperforms other similar models since it consider the interaction between models. This means that it ensures reliable perdition, taking the highly uncertain factor (e.g., the COVID-19) into account. © 2023 Elsevier Ltd
ABSTRACT
The positive role of clinical decision support systems based on clinical guidelines in reducing medical errors and improving patient outcomes has been widely recognized. However, the knowledge in clinical guidelines is usually hard-coded into clinical decision support systems, making it difficult for these systems to adapt to the rapid changes of clinical guidelines. Knowledge being hard-coded into the system also means that the system is a black box, and doctors cannot understand the decision-making logic behind the system. These reasons make it difficult for clinical decision support systems to be applied on a large scale. This paper proposes a flexible clinical decision support model, which contains two key parts, namely the knowledge authoring environment and the knowledge execution environment. The transition of knowledge from hard-coded to flexible editing is illustrated in the COVID-19 case. This flexible method will be applied to more complex clinical problems in the future. © 2021 IEEE.
ABSTRACT
This paper reports on the development of a model of COVID-19 transmission dynamics that takes into account a comprehensive mitigation protocol. This is necessary for public health decision support and making actionable recommendations on COVID-19 response. The comprehensive mitigation protocol includes (1) personal protection and social distancing, (2) use of smart applications for symptom reporting and contact tracing, (3) targeted testing based on identification of individuals with possible exposure and/or infection via symptom reporting and contact tracing, (4) surveillance testing, and (5) shelter, quarantine and isolation procedures. The proposed model (1) extends a common epidemiological discrete dynamic model with the comprehensive mitigation protocol, (2) uses Bayesian probability analysis to estimate the conditional probabilities of being in non-circulating epidemiological sub-compartments as a function of the mitigation protocol parameters, based on which it (3) estimates transition ratios among the compartments, and (4) computes a range of key performance indicators including health outcomes, mitigation cost and productivity loss. The proposed model can serve as a critical component for COVID-19 mitigation decision support and recommender systems, as part of a broader effort to support urgent pandemic response. © 2021 IEEE.